The following figures have been blessed for the Neural Network based Jet Charge Tagger.

We define signal tracks to be all the B daughters, including the decay products of unstable B daughters.
We define background tracks to be all the other tracks in the event.

The jet in the event with a high content of B daughters is the signal jet. All the jets that are not signal are background jets.
More text can be found in CDF note 7482.

Figure:Input variables for the track probability Neural Network for tracks with a hit in the silicon layer closest to the interaction point (left hand side) and tracks without (right hand side).
Top row: track transverse momentum. The peak at 1 GeV is given by the requirement of a minimum of 1 GeV to seed jets. Center row: transverse momentum of the jet to which the track is assigned. Bottom row: track impact parameter.

Figure:Input variable for the track probability Neural Network: Signed impact parameter significance for tracks with a hit in the silicon layer closest to the interaction point (left) and tracks without (right). The impact parameter is lifetime signed with respect to the jet momentum.

Figure:Input variable for the track probability Neural Network: Flag that tells if the track was used to fit the primary vertex (PV) of the event for tracks with a hit in the silicon layer closest to the interaction point (left) and tracks without (right). The value 0 means that the track was not used for the PV; the value 1. means that it was used and 2. represents the cases in which the PV was not found.

Figure:Input variable for the track probability Neural Network: cone angle between the track and the B candidate direction, identified as the sum of the trigger lepton and displaced track momenta, for tracks with a hit in the silicon layer closest to the interaction point (left) and tracks without (right).

Figure:Input variable for the track probability Neural Network: rapidity of the track with respect to the jet axis for tracks with a hit in the silicon layer closest to the interaction point (left) and tracks without (right). The rapidity is defined as , where is the longitudinal component of the track with respect to the jet axis,

Figure: Output of the track probability Neural Network for tracks with a hit in the silicon layer closest to the interaction point (left) and tracks without (right).

Figure:Left: Performance plot for the track probability Neural Network for tracks with and without L00 hits. Each point in the graph corresponds to a cut on the network output. The performance is better for tracks with L00 hits thanks to the higher resolution on and . Right: comparison between the performance of a cut on the track probability Neural Network output and a cut on the track probability based on displacement information only. Tracks with and without L00 hits are not distinguished in this case, meaning that the solid line is equivalent to a weighted sum of the curves in the left graph.

Figure:Input variables for the jet probability Neural Network:Left: Number of tracks in the secondary vertex. The peak at . is relative to jets for which no secondary vertex is found. Right: Probability of the fit of the secondary vertex.

Figure:Input variables for the jet probability Neural Network:Left: transverse momentum of the jet. Right: number of tracks in the jet.

Figure:Input variables for the jet probability Neural Network:Left: angle between the jet and the direction of the B candidate, identified by the trigger lepton and the displaced track, in the plane transverse to the beam line. Right: Transverse momentum of the track with the maximum in the jet.

Figure:Input variables for the jet probability Neural Network:Left: invariant mass of the jet Right: jet spread, defined as the sum of the angles in space between the tracks and the jet axis. This quantity gives information on how collimated the jet is. The peak at 1. is relative to jets constituted by a single track.

Figure:Input variables for the jet probability Neural Network:Left: sum of the projections of the track moments along the jet axis. Right: sum of the track momentum components perpendicular to the jet axis.

Figure:Input variables for the jet probability Neural Network:Left: number of tracks in the jet that have a probability greater than 50% to be a b-hadron decay product. Right: sum of the probabilities of the tracks in the jet.

Figure:Input variables for the jet probability Neural Network:Left: probability of the track with the highest probability in the jet. Right: Neural Network tag based on invariant mass, . In order to compute this variable, the invariant mass of the tracks with the highest probability is computed. The tracks are progressively summed up until the invariant mass becomes larger than the mass. The tag variable is then the probability of the track that pushes the invariant mass value above the mass.

Figure:Input variables for the jet probability Neural Network:Left: likelihood combination of the probabilities of the tracks in the jet. Right: likelihood combination of the probabilities of the tracks with in the jet.

Figure: Performance graph for a cut on the jet Neural Network output (solid line) in simulated events. The comparison with the jet probability obtained by combining with a likelihood the track probability based only on impact parameter is displayed (dashed line).

Figure: Jet charge for the different types of jet for the e+SVT sample, shown separately for positive and negative electrons. The shift between the two distributions is more evident for Class 1 jets.

Figure: Jet charge for the different types of jet for the +SVT sample, shown separately for positive and negative muons. The shift between the two distributions is more evident for Class 1 jets.

Figure: Dependency of dilution on , where is the jet charge and is the jet probability. The plots are relative to the e+SVT sample.

Figure: Dependency of dilution on , where is the jet charge and is the jet probability. The plots are relative to the +SVT sample